store = {}
store['args']={'name': 'emnist_independent_bald_k10_218487', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 218487, 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 8, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=8', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.02303191489361702, 'nll': 3.861316835119369}, 'chosen_samples': [48273, 9329, 101556, 19256, 47093], 'chosen_samples_score': [0.008359429612755598, 0.008420767635106685, 0.008450803533196272, 0.008555319160223007, 0.009501149877905846], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.036808510638297876, 'nll': 58.36473187385722}, 'chosen_samples': [31218, 15780, 14774, 50260, 43594], 'chosen_samples_score': [0.8911400701161297, 0.8932277711807517, 0.8943067276778724, 0.8992966080615588, 0.9136194887429265], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06345744680851063, 'nll': 43.78238219565534}, 'chosen_samples': [75531, 68837, 2090, 25594, 68279], 'chosen_samples_score': [1.0606403947755592, 1.0699997205011007, 1.1049333942013901, 1.11624331134008, 1.1251985133152553], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.07228723404255319, 'nll': 45.63406155850025}, 'chosen_samples': [11893, 97195, 88917, 79728, 27470], 'chosen_samples_score': [1.595235441609217, 1.598656933028822, 1.6012928383502825, 1.6047883493924044, 1.6081100987112824], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.08063829787234042, 'nll': 38.342237279364404}, 'chosen_samples': [72019, 88904, 67908, 110952, 68325], 'chosen_samples_score': [1.4586417731808117, 1.4601806761178209, 1.4662168852432582, 1.4704127149366184, 1.4779896687542975], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.09686170212765957, 'nll': 36.9847173520352}, 'chosen_samples': [52707, 52791, 10935, 66111, 22392], 'chosen_samples_score': [1.5531890968167246, 1.5633265454883256, 1.5723012620845664, 1.599699070806244, 1.5998138029098785], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.10180851063829788, 'nll': 33.09758233739975}, 'chosen_samples': [72892, 42732, 71638, 95725, 63628], 'chosen_samples_score': [1.3511039716680602, 1.3532707554665326, 1.3554945642214853, 1.4044410518216313, 1.5063984836510937], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.11686170212765958, 'nll': 28.401350449907017}, 'chosen_samples': [57871, 54737, 108689, 103256, 91794], 'chosen_samples_score': [1.5577422940458279, 1.5612122340763093, 1.576536691355876, 1.5649010224797064, 1.5649170670728854], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1305851063829787, 'nll': 25.22176465298267}, 'chosen_samples': [35104, 61842, 4005, 92935, 7883], 'chosen_samples_score': [1.4794831680355816, 1.5025026015102545, 1.5317419165537076, 1.5129270654225424, 1.48581200935192], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.14361702127659576, 'nll': 25.54394536119826}, 'chosen_samples': [55771, 26306, 1857, 83561, 1622], 'chosen_samples_score': [1.7167780834447135, 1.7277787029128218, 1.735185061947842, 1.7485179270786817, 1.7396685365499407], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14345744680851064, 'nll': 21.876851663792387}, 'chosen_samples': [62588, 62138, 81759, 92937, 21918], 'chosen_samples_score': [1.4198625437086434, 1.4200409753479337, 1.4303273733470445, 1.4594304975441097, 1.5062141173405763], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.16585106382978723, 'nll': 26.0171126523931}, 'chosen_samples': [9830, 23781, 76900, 56594, 70422], 'chosen_samples_score': [1.6877912501653003, 1.701552069771572, 1.7019602086392416, 1.764834775076663, 1.706436193884227], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.16132978723404257, 'nll': 21.539153469978494}, 'chosen_samples': [86442, 98992, 29026, 100713, 14679], 'chosen_samples_score': [1.76314553855218, 1.7795811321840198, 1.8275879454299657, 1.7865941625228572, 1.786560965417734], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17845744680851064, 'nll': 21.33329857846524}, 'chosen_samples': [133, 37254, 80730, 90294, 98909], 'chosen_samples_score': [1.4811696781634722, 1.5156306485849276, 1.5420869417106617, 1.5141135044874237, 1.5813284952054465], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18335106382978725, 'nll': 19.150536372407952}, 'chosen_samples': [78704, 72698, 104197, 18448, 10382], 'chosen_samples_score': [1.6327363157516457, 1.6444499965177957, 1.6523541944122333, 1.7203376016655305, 1.788498000624497], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18824468085106383, 'nll': 20.207928843396775}, 'chosen_samples': [52660, 20988, 43468, 100017, 72531], 'chosen_samples_score': [1.6677145441063908, 1.7227977307830478, 1.7110099912808736, 1.6859608916333915, 1.8784209025129663], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.20622340425531915, 'nll': 17.67071236143721}, 'chosen_samples': [104655, 16339, 71762, 34055, 74545], 'chosen_samples_score': [1.595048613945917, 1.6647574930112072, 1.598455090484211, 1.7245684656661044, 1.6152547390725576], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.2048936170212766, 'nll': 17.397198991166785}, 'chosen_samples': [17118, 55262, 64413, 56127, 21255], 'chosen_samples_score': [1.7497037931027932, 1.7636839527040706, 1.7744106342271238, 1.7892272394061637, 1.7941813572124257], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22893617021276597, 'nll': 17.737240931734124}, 'chosen_samples': [74578, 68615, 90310, 80367, 64414], 'chosen_samples_score': [1.6087929983996592, 1.6345440496401789, 1.6578953725962002, 1.7670850816987826, 1.7486256767405595], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23585106382978724, 'nll': 16.49192933671018}, 'chosen_samples': [80834, 42600, 30884, 61562, 71196], 'chosen_samples_score': [1.7268605008495412, 1.7367214293023492, 1.7919392351209362, 1.7458328453179703, 1.726940332774749], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23930851063829786, 'nll': 14.826782556899051}, 'chosen_samples': [91694, 9945, 28812, 435, 50638], 'chosen_samples_score': [1.4913763260324513, 1.5953294684455195, 1.4975324198153523, 1.5010089593196003, 1.5337883328451585], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26882978723404255, 'nll': 14.804856271135046}, 'chosen_samples': [4448, 690, 9918, 10457, 105401], 'chosen_samples_score': [1.5567831235610992, 1.5673627135046215, 1.5700956022374144, 1.5873657306386777, 1.5935035116835299], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26595744680851063, 'nll': 14.357244332496157}, 'chosen_samples': [62181, 16344, 111112, 99188, 91938], 'chosen_samples_score': [1.7501871979236239, 1.756330887836473, 1.7929644368200126, 1.8173239390496976, 1.8842204391138748], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.26941489361702126, 'nll': 14.832311835593366}, 'chosen_samples': [15953, 94027, 19724, 29901, 46408], 'chosen_samples_score': [1.6902919198135755, 1.7448783526285925, 1.7553378652993623, 1.717105352023229, 1.700524980601454], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.27702127659574466, 'nll': 14.644009456228703}, 'chosen_samples': [18381, 27253, 81285, 111051, 96864], 'chosen_samples_score': [1.5858716795848207, 1.59046958234268, 1.6263435265533495, 1.6054484916152159, 1.6017534786560783], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2775531914893617, 'nll': 12.708417209868735}, 'chosen_samples': [100972, 100440, 40616, 41436, 33394], 'chosen_samples_score': [1.5987285119797632, 1.604297104657983, 1.6125616767401298, 1.6588659091857674, 1.7174060216142881], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28382978723404256, 'nll': 13.3271992346581}, 'chosen_samples': [358, 107092, 17630, 112798, 68137], 'chosen_samples_score': [1.5436743189842264, 1.5507422553264907, 1.5697093639262396, 1.5775009790045682, 1.569109776464698], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29781914893617023, 'nll': 11.49548071840976}, 'chosen_samples': [81931, 109586, 9415, 87703, 77163], 'chosen_samples_score': [1.7922734956127386, 1.7980235063463375, 1.8245023611230804, 1.8249895282515483, 1.8356610836497054], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.301436170212766, 'nll': 12.112376089704798}, 'chosen_samples': [85511, 16556, 97705, 16804, 12675], 'chosen_samples_score': [1.6385213713844275, 1.689430893200362, 1.6906081833980449, 1.725442923944711, 1.643042944416401], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.31574468085106383, 'nll': 10.554283998367634}, 'chosen_samples': [64805, 9989, 112079, 75024, 37919], 'chosen_samples_score': [1.6046446406640227, 1.617120169639206, 1.6710653688932742, 1.695670317170485, 1.6190592029180666], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.33670212765957447, 'nll': 10.175227230153185}, 'chosen_samples': [68160, 36058, 105673, 106402, 9486], 'chosen_samples_score': [1.5892498859974329, 1.6533095260612076, 1.643797199070006, 1.5941899366336019, 1.5900473475506143], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.34425531914893615, 'nll': 10.990155813338909}, 'chosen_samples': [74019, 81594, 84802, 36282, 74318], 'chosen_samples_score': [1.6664808886043891, 1.6766822953396883, 1.703885264677019, 1.698321133956112, 1.7286641409880565], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.351968085106383, 'nll': 9.697595700203104}, 'chosen_samples': [59669, 95077, 4922, 41593, 7785], 'chosen_samples_score': [1.6559220748893795, 1.6722582454624226, 1.684953769261558, 1.6930629053244455, 1.7371798603262532], 'chosen_samples_orignal_score': None})
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